15 research outputs found
Dictionary-based classifiers for exploiting feature sequence information and their application to hyperspectral remotely sensed data
No full textThe problem of classification is shared across various disciplines.
Designing even less computationally demanding and more effective
classifiers has been a key challenge for researchers for many years. No
single classifier can be highly effective for all types of datasets and
thus, depending on the data distribution, various classifiers have
been proposed in the literature. To our knowledge, feature values
have been vastly exploited as the base for discriminating classes,
while feature sequence information has been somehow underexploited so far. In the proposed approach normalised features are
sorted and ranked, creating a sequence of finite numbers. The associated rank of the created sequence is used as an additional feature,
which in a way defines the sample-specific intra-feature relationship.
Three novel dictionary-based approaches such as Sequence Classifier
(SC), Sequence-dictionary-based k-Nearest Neighbours Classifier
(SDk-NN) and Combined-dictionary-based k-Nearest Neighbours
Classifier (CDk-NN) are proposed in this paper.
In the case of remotely sensed data, and specifically in HyperSpectral Images (HSI), the spectral features (Spectral signatures)
represent a strong, object-specific spectral relationship, which is
a key point in our proposed approach. In this case, indeed, the
proposed classifiers were tested over various (five) HS datasets and
found to be effective. Based on the classifiers features, two derived
distance measures are proposed and validated for the HS dataset,
namely: the Normalised Sequence Distance (NSD) measure and
Combined Distance (CD) measure. These measures appear to overperform the conventional Normalised Euclidean Distance (NED) in
this context. Also, validation for both binary and multi-class datasets
are experimented and their performances are evaluated in terms of
accuracy and other standard measures. Experimental results over 21
datasets revealed that the proposed approaches perform comparably, and in some cases even better than other classifiers. Stackoperated, class-specific sparse dictionaries are also introduced in
order to reduce the computational complexity, which can be used
as an active learning-based approach for optimal training sample
selection. Additional tests were performed with variable levels of
dictionary sparsity for assessing its impact on accurac
Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
No full textIn the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods have been proposed to perform it. Recently, Singular Spectrum Analysis (SSA) and its 2-D variation (2D-SSA) have emerged as popular, cutting-edge technologies for effective feature extraction in Hyperspectral Images (HSI). Using 2D-SSA, each band image of an HSI is initially decomposed into various components, and then the image is reconstructed using the most significant eigen-tuples relative to their eigen-values, which represent strong spatial features for the classification task. However, instead of performing reconstruction on the whole image, it may be more effective to apply reconstruction to object-specific spatial regions, which is the proposed objective of this research. As an HSI may cover a large area, multiple objects are generally present within a single scene. Hence, spatial information can be highlighted accurately by specializing the reconstruction based on the local context. The local context may be defined by the so-called superpixels, i.e., finite sets of pixels that constitute a homogeneous set. Each superpixel may undergo tailored reconstruction, with a process expected to perform better than non-spatially-adaptive approaches. In this paper, a Superpixel-based SSA (SP-SSA) method is proposed where the image is first segmented into multiple regions using a superpixel segmentation approach. Next, each segment is individually reconstructed using 2D-SSA. In doing so, the spatial contextual information is preserved, leading to better classifier performance. The performance of the reconstructed features is evaluated using an SVM classifier. Experiments on four popular benchmark datasets reveal that, in terms of the classification accuracy, the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods
Conditional nearest regularized subspace classifiers: a fast classification approach for HSI
No full textClassification is an important problem in a large variety of applications, which makes it an open-ended forumfor researchers in various disciplines. In this paper, the proposed two approaches are mostly focused on Nearest Regularized Subspace (NRS) classifier. The proposed
pair of variants are: (1) reducing the computational complexity of NRS classifier termed as Conditional Fast Nearest Regularized
Subspace (CFNRS) classifier and (2) incorporation of dissimilarity feature termed as Conditional Dissimilarity-based Nearest
Regularized Subspace (CDNRS) classifier. Regarding the first approach, the simple k-NN classifier result is used as a condition to
evaluate NRS classifier. Regarding the second approach, an intrafeature dissimilarity measure is considered to create a pair of dictionary
which contains a conditional binary matrix and a dissimilarity feature matrix. Each conditional binary matrix is a collection of distinct
sub-spaces representing their respective classes. The incoming data is classified with the help of a dictionary and classification
performance is validated over other state-of-art approaches. As Hyper-Spectral (HS) data have a strong spectral correlation in terms
of spectral signatures, various such HS data are included for validation of our approach. The experimental study reveals the pros and
cons of different classifiers along with the effectiveness of our proposed approach. The proposed conditional classifier is found to be
able to compete with other, standard classifiers in terms of accuracy and computational complexity, mostly where features possess some
sort of sequence information. The suitability of the proposed classifier is also verified for both binary and multi-class classification problems.
Along with the experimental validation, the statistical significance of the proposed conditional measure is assessed over other standard
state-of-art methods, and the proposed one outperformed the others. The experimental results reveal the importance of dissimilarity
features for classification and also suggest to incorporate a simple classifier such as k-NN, prior to any other classifier. This is mainly
meant to reduce the computational complexity of the selected classifier without compromising the accuracy
Development of transition metal dichalcogenide based quantum dots for light emitting diodes
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Filtering based Spatial-Spectral Feature Extraction for Hyperspectral Image Classification
No full textSuperpixel Clustering Based Segmentation Algorithm for Hyperspectral Image Classification
No full textPSO-Based Synthetic Minority Oversampling Technique for Classification of Reduced Hyperspectral Image
No full textProbabilistic Histogram-Based Band Selection and Its Effect on Classification of Hyperspectral Images
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